Upload configuration_bailing_moe_v3.py with huggingface_hub

configuration_bailing_moe_v3.py

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+"""Bailing MoE V2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class BailingMoeV3Config(PretrainedConfig):
+
+    def __init__(
+        self,
+        vocab_size=157184,
+        hidden_size=2048,
+        intermediate_size=5120,
+        num_hidden_layers=20,
+        num_attention_heads=16,
+        num_key_value_heads=4,
+        hidden_act="silu",
+        use_qkv_bias=False,  # bailing only
+        use_bias=False,  # bailing only
+        rms_norm_eps=1e-06,
+        tie_word_embeddings=False,  # PretrainedConfig key, here change default value.
+        embedding_dropout=0.0,
+        attention_dropout=0.0,
+        output_dropout=0.0,
+        initializer_range=0.02,
+        max_position_embeddings=32768,
+        rope_theta=600000.0,
+        use_cache=True,
+        max_window_layers=20,
+        rope_scaling=None,
+        pad_token_id=156892,
+        eos_token_id=156892,
+        num_experts=256,
+        num_shared_experts=1,
+        num_experts_per_tok=8,
+        n_group=8,
+        topk_group=4,
+        moe_intermediate_size=512,
+        moe_shared_expert_intermediate_size=512,
+        first_k_dense_replace=1,
+        head_dim=128,
+        output_router_logits=False,
+        use_qk_norm=True,
+        num_nextn_predict_layers=0,
+        mtp_loss_scaling_factor=0,
+        moe_router_enable_expert_bias=True,
+        routed_scaling_factor=1.0,
+        layer_group_size=5,
+        kv_lora_rank=512,
+        q_lora_rank=None,
+        qk_rope_head_dim=64,
+        v_head_dim=128,
+        qk_nope_head_dim=128,
+        rope_interleave=True,
+        score_function="sigmoid",
+        scoring_func="sigmoid",
+        seq_aux=True,
+        topk_method="noaux_tc",
+        router_dtype="fp32",
+        gated_attention_proj_granularity_type=None,
+        no_kda_lora=False,
+        kda_safe_gate=False,
+        kda_lower_bound=None,
+        short_conv_kernel_size=4,
+        pregate_enabled=False,
+        pregate_hidden=512,
+        pregate_inference=False,
+        pregate_use_prev_topk=True,
+        pregate_init_router=False,
+        pregate_use_prev_token=True,
+        pregate_shallow_hidden=1024,
+        pregate_shallow_layers=5,
+        pregate_shallow_loss_weight=1.5,
+        pregate_start_layer=7,
+        # v6: cross-token pre-gate.  pregate_N at position t is trained /
+        # consumed to route layer N+1 at position t+1 (one-token-ahead),
+        # matching the MLX single-sync fast path.  False keeps the v5
+        # same-token semantics.
+        pregate_cross_token=False,
+        # Top-k OPD (on-policy distillation) for the pre-gate modules.
+        # strategy: "none" (full-vocab KL, legacy) | "union" | "only_stu" |
+        # "only_tch" | "intersection".  The support is always the top-8 expert
+        # sets selected by the student pre-gate / teacher router, so the KL is
+        # sparse over <=16 experts instead of all 128.
+        pregate_opd_strategy="none",
+        pregate_opd_topk=8,
+        pregate_opd_temperature=1.0,
+        pregate_opd_weight=1.0,
+        pregate_opd_ce_weight=1.0,
+        pregate_opd_weight_mode="teacher_p",
+        **kwargs,
+    ):
+        self.num_hidden_layers = num_hidden_layers
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.use_qkv_bias = use_qkv_bias
+        self.use_bias = use_bias
+        self.rms_norm_eps = rms_norm_eps
+        self.embedding_dropout = embedding_dropout
+        self.attention_dropout = attention_dropout
+        self.output_dropout = output_dropout
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+        self.mtp_loss_scaling_factor = mtp_loss_scaling_factor
+        self.initializer_range = initializer_range
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        self.use_cache = use_cache
+        self.max_window_layers = max_window_layers
+        self.head_dim = head_dim or self.hidden_size // self.num_attention_heads
+        self.rope_scaling = rope_scaling
+        self.use_qk_norm = use_qk_norm
+        self.moe_router_enable_expert_bias = moe_router_enable_expert_bias
+        self.routed_scaling_factor = routed_scaling_factor
+
+        # MoE configs
+        self.num_experts = num_experts
+        self.num_shared_experts = num_shared_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size
+        self.first_k_dense_replace = first_k_dense_replace
+        self.output_router_logits = output_router_logits
+
+        # Linear configs
+        self.layer_group_size = layer_group_size
+        # mla
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+
+        self.score_function = score_function
+        self.scoring_func = scoring_func
+        self.seq_aux = seq_aux
+        self.topk_method = topk_method
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
+        self.rope_interleave = rope_interleave
+        self.router_dtype = router_dtype
+        self.gated_attention_proj_granularity_type = gated_attention_proj_granularity_type
+        self.no_kda_lora = no_kda_lora
+        self.kda_safe_gate = kda_safe_gate
+        self.kda_lower_bound = kda_lower_bound
+        self.short_conv_kernel_size = short_conv_kernel_size
+        # Pre-gated MoE (arXiv 2308.12066): layer N's pre-gate selects the
+        # experts for MoE layer N+1.  pregate_enabled turns the modules on;
+        # pregate_inference makes the deployed model use the pre-gate as the
+        # router (the first MoE layer keeps its original gate, zero lead).
+        self.pregate_enabled = pregate_enabled
+        self.pregate_hidden = pregate_hidden
+        self.pregate_inference = pregate_inference
+        self.pregate_use_prev_topk = pregate_use_prev_topk
+        self.pregate_init_router = pregate_init_router
+        self.pregate_use_prev_token = pregate_use_prev_token
+        self.pregate_shallow_hidden = pregate_shallow_hidden
+        self.pregate_shallow_layers = pregate_shallow_layers
+        self.pregate_shallow_loss_weight = pregate_shallow_loss_weight
+        self.pregate_start_layer = pregate_start_layer
+        self.pregate_cross_token = pregate_cross_token
+        self.pregate_opd_strategy = pregate_opd_strategy
+        self.pregate_opd_topk = int(pregate_opd_topk)
+        self.pregate_opd_temperature = float(pregate_opd_temperature)
+        self.pregate_opd_weight = float(pregate_opd_weight)
+        self.pregate_opd_ce_weight = float(pregate_opd_ce_weight)
+        self.pregate_opd_weight_mode = pregate_opd_weight_mode
+        super().__init__(
+            pad_token_id=pad_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
+        )